On the use of convolutional neural networks for robust classification of multiple fingerprint captures

Peralta, Daniel; Triguero, Isaac; García, Salvador; Saeys, Yvan; Benítez, José Manuel; Herrera, Francisco

doi:10.1002/int.21948

Cited by 73 publications

(54 citation statements)

References 63 publications

(138 reference statements)

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“…The set of valid transformations that improves the performance of the CNN-model depends on the particularities of the problem. Several previous studies have demonstrated that increasing the size of the training dataset using different data-augmentation techniques increases performance and makes the learning of CNNs models robust to changes in scales, brightness and geometrical distortions [44,47].…”

Section: Training Phase: Cnn-classifier With Fine-tuning and Data Augmentioning

confidence: 99%

Deep-learning Versus OBIA for Scattered Shrub Detection with Google Earth Imagery: Ziziphus lotus as Case Study

et al. 2017

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There is a growing demand for accurate high-resolution land cover maps in many fields, e.g., in land-use planning and biodiversity conservation. Developing such maps has been traditionally performed using Object-Based Image Analysis (OBIA) methods, which usually reach good accuracies, but require a high human supervision and the best configuration for one image often cannot be extrapolated to a different image. Recently, deep learning Convolutional Neural Networks (CNNs) have shown outstanding results in object recognition in computer vision and are offering promising results in land cover mapping. This paper analyzes the potential of CNN-based methods for detection of plant species of conservation concern using free high-resolution Google Earth TM images and provides an objective comparison with the state-of-the-art OBIA-methods. We consider as case study the detection of Ziziphus lotus shrubs, which are protected as a priority habitat under the European Union Habitats Directive. Compared to the best performing OBIA-method, the best CNN-detector achieved up to 12% better precision, up to 30% better recall and up to 20% better balance between precision and recall. Besides, the knowledge that CNNs acquired in the first image can be re-utilized in other regions, which makes the detection process very fast. A natural conclusion of this work is that including CNN-models as classifiers, e.g., ResNet-classifier, could further improve OBIA methods. The provided methodology can be systematically reproduced for other species detection using our codes available through (https://github.com/EGuirado/CNN-remotesensing).

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Section: Training Phase: Cnn-classifier With Fine-tuning and Data Augmentioning

confidence: 99%

Deep-learning Versus OBIA for Scattered Shrub Detection with Google Earth Imagery: Ziziphus lotus as Case Study

et al. 2017

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“…Contrary to the statistical and model‐based practices, ANNs are self‐adaptive in inferring underlying functional associations from the input data . Several nonlinear hidden layers between input and output modules are introduced, supporting the system to learn complex functions and features, for better predictions …”

Section: Predicting Withdrawals In Vlementioning

confidence: 99%

Virtual learning environment to predict withdrawal by leveraging deep learning

et al. 2019

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The current evolution in multidisciplinary learning analytics research poses significant challenges for the exploitation of behavior analysis by fusing data streams toward advanced decision-making. The identification of students that are at risk of withdrawals in higher education is connected to numerous educational policies, to enhance their competencies and skills through timely interventions by academia. Predicting student performance is a vital decision-making problem including data from various environment modules that can be fused into a homogenous vector to ascertain decisionmaking. This research study exploits a temporal sequential classification problem to predict early withdrawal of students, by tapping the power of actionable smart data in the form of students' interactional activities with the online educational system, using the freely available Open University Learning Analytics data set by employing deep long short-term memory (LSTM) model. The deployed LSTM model outperforms baseline logistic regression and artificial neural networks by 10.31% and 6.48% respectively with 97.25% learning accuracy, 92.79% precision, and 85.92% recall. K E Y W O R D S classification, deep learning, long short-term memory (LSTM), students-at-risk. smart data, virtual learning environment (VLE) Int J Intell Syst. 2019;34:1935-1952.wileyonlinelibrary.com/journal/int

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“…A larger dataset could be constructed from a small number of initial samples with data argumentation. Using different data argumentation methods to increase the size of the training dataset increased the generalisation performance of the model, reduced the risk of overfitting, and made the CNN model more robust to the scale, brightness, and geometric distortions [28,29].…”

Section: Data Samplingmentioning

confidence: 99%

Progressive Cascaded Convolutional Neural Networks for Single Tree Detection with Google Earth Imagery

et al. 2019

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High-resolution remote sensing images can not only help forestry administrative departments achieve high-precision forest resource surveys, wood yield estimations and forest mapping but also provide decision-making support for urban greening projects. Many scholars have studied ways to detect single trees from remote sensing images and proposed many detection methods. However, the existing single tree detection methods have many errors of commission and omission in complex scenes, close values on the digital data of the image for background and trees, unclear canopy contour and abnormal shape caused by illumination shadows. To solve these problems, this paper presents progressive cascaded convolutional neural networks for single tree detection with Google Earth imagery and adopts three progressive classification branches to train and detect tree samples with different classification difficulties. In this method, the feature extraction modules of three CNN networks are progressively cascaded, and the network layer in the branches determined whether to filter the samples and feed back to the feature extraction module to improve the precision of single tree detection. In addition, the mechanism of two-phase training is used to improve the efficiency of model training. To verify the validity and practicability of our method, three forest plots located in Hangzhou City, China, Phang Nga Province, Thailand and Florida, USA were selected as test areas, and the tree detection results of different methods, including the region-growing, template-matching, convolutional neural network and our progressive cascaded convolutional neural network, are presented. The results indicate that our method has the best detection performance. Our method not only has higher precision and recall but also has good robustness to forest scenes with different complexity levels. The F1 measure analysis in the three plots was 81.0%, which is improved by 14.5%, 18.9% and 5.0%, respectively, compared with other existing methods.

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On the use of convolutional neural networks for robust classification of multiple fingerprint captures

Cited by 73 publications

References 63 publications

Deep-learning Versus OBIA for Scattered Shrub Detection with Google Earth Imagery: Ziziphus lotus as Case Study

Deep-learning Versus OBIA for Scattered Shrub Detection with Google Earth Imagery: Ziziphus lotus as Case Study

Virtual learning environment to predict withdrawal by leveraging deep learning

Progressive Cascaded Convolutional Neural Networks for Single Tree Detection with Google Earth Imagery

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